Effect of Geographical Diversification on Informational Efficiency … · geographical diversification with similar business operations in different countries as the main corporate

Capital Markets Review Vol. 24, No.1, pp. 16-37 (2016) ISSN 1823-4445

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Effect of Geographical Diversification on Informational

Efficiency in Emerging Countries

Suan Poh* & Chee-Wooi Hooy

School of Management, Universiti Sains Malaysia

Abstract: This study investigates the effect of geographical diversification on

informational efficiency. Informational efficiency is measured using the price delay

measure and is further divided into informational efficiency related to local news and

informational efficiency related to global news. Geographical diversification is

proxied using four different variables – foreign sales dummy, number of foreign

countries, foreign sales ratio and the Herfindahl Index. The sample study involves

public listed companies from 12 emerging countries for the period from year 2005 to

year 2014. The regression results prove that all four geographical diversification

proxies show a positive and significant effect on local price delay. This proves that

when a company undergoes geographical diversification, its business structure will

become complex. Few investors will focus on a geographical diversified company,

which leads to lower informational efficiency. All the regression models in this study

are robust to heteroscedasticity and multicollinearity problems. All the geographical

diversification proxies remain significant towards local delay when alternative delay

measures are used, and financial crisis is controlled by using a crisis dummy.

Keywords: Informational efficiency, Price delay, Geographical diversifications,

Emerging markets

JEL Classification: F23, G14, G15

1. Introduction

Over the past 25 years, increasingly integrated capital markets and globalization have

lowered the cost of companies doing business in foreign markets. Foreign investment made

by corporations in the industrialized nations has grown dramatically. Generally, firms adopt

geographical diversification with similar business operations in different countries as the

main corporate strategy to gain competitive advantages (Barney and Hesterly 2008; Chang

and Wang 2007; Hitt et al. 1997). For example, large publicly traded US and EU firms

operate their businesses, on average, in more than three different geographic markets

(Bodnar et al. 1999; Pavelin and Barry 2005).

Geographical diversification is said to confer a number of advantages, including full

use of resources and distribution of costs on the basis of the growing market and product

range, which lead to economies of scale (Ghoshal 1987). To the extent that firms are able to

leverage their operations worldwide, international investment may enable them to capture

valuable operating synergies (Feinberg and Phillips, 2002). Human capital in multinational

companies can learn and innovate faster (Bartlett and Ghoshal 2000). Companies that are

able to expand their business globally can gain access to specific skills at lower cost (Kogut

1985; Porter 1986). Companies can also shift their production lines to other countries with

lower labour costs (Kogut and Kulatilaka 1994).

This paper departs from the traditional focus of geographical diversification on benefits

and cost of firm values to a relatively less explored area, the informational efficiency of

stock markets. Market efficiency can be defined as the extent and speed that market prices

of tradable assets incorporate the available information. High market efficiency means that

the process of incorporating information into market prices is fast and complete.

* Corresponding author: Suan Poh. Email: [email protected]

Effect of Geographical Diversification on Informational Efficiency in Emerging Countries

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Since Professor Eugene Fama introduced his Efficient Market Hypothesis (EMH),

“informational efficiency” has become a common term in financial studies. Informational

efficiency is used to indicate the extent that market prices of tradable assets incorporate the

available information. After the theory of EMH was introduced, researchers started to test

the EMH theory in the stock markets around the world. In the early part of the research,

researchers only focused on testing whether or not a market was efficient. They used many

different methods to test EMH, such as serial correlation tests (Fama 1965), spectral

analysis as used by Granger and Morgenstern (1963), and the variance ratio test (Lo and

MacKinlay 1988).

In 1997, Campbell et al. (1997) offered the concept of “relative efficiency”, which is

the informational efficiency of a market measured relative to another market. Not only can

we indicate whether a market is efficient or not, but also the lead-lag relationship between

any two markets can be determined. However, it is still not possible to quantify the degree

of efficiency of a market. Hou and Moskowitz (2005) proposed a price delay model that can

quantify the informational efficiency of a market. This price delay model has several

advantages compared to conventional tests. First, it permits researchers to identify various

factors that affect the informational efficiency of stocks. Second, it enables researchers to

measure the adjustment of stock price to both local and global market information.

Since this price delay measure has been proposed, many studies have been done to

investigate the determinants of informational efficiency. Basically, the determinants of

informational efficiency can be divided into two sections – stock specification and firm

fundamental. Considerable research has been done on variables about stock specifications,

for example, trading volume by Chordia and Swaminathan (2004), the liberalization process

by Bae et al. (2012), liquidity by Lesmond (2005), short selling by Saffi and Sigurdsson

(2011), option pricing by Phillips (2011), and market frictions by Hou and Moskowitz

(2005). However, only a few researchers have studied the relationship between firm

fundamentals and informational efficiency, such as firm size by Hou and Moskowitz (2005),

accounting quality by Callen et al. (2013) and analyst coverage by Bae et al. (2012). The

price delay model is still incomplete, as there are still many other undiscovered factors that

affect the informational efficiency of a firm.

This study focuses on public listed companies from emerging markets to study the

effect of geographical diversification on informational efficiency. Emerging countries

present an interesting case study for geographical diversification because there have been a

huge increase in the outflows of foreign direct investment (OFDI) from emerging markets in

the 2000s.

OFDI streams from emerging market multinational enterprises (MNEs), which are

indicated in Table 1 by companies from both developing countries and transition economies,

have demonstrated especially dynamic growth rates of roughly 300% from US$159 billion

in 2005 (US$140 billion from developing countries and US$19 billion from transition

economies), to reach approximately US$482 billion in 2012 (US$426 billion from

developing countries and US$55 billion from transition economies).

In recent decades, the OFDI of emerging countries has changed tremendously in terms

of regional distribution. Emerging market MNEs have increased their foreign investment in

many other developing countries. They have progressively increased the resources

allocation in developed countries. Among all the sectors, firms that export natural resources

and service firms make up the highest percentage of OFDI (World Investment Report 2008).

In this study, the relationship between geographical diversification and informational

efficiency is investigated. We further divide informational efficiency into local information

and global information, which is discussed separately in hypotheses 1 and 2. Each

hypothesis is further divided into four subsections that represent four different proxies for

Suan Poh and Chee-Wooi Hooy

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geographical diversification – foreign sales dummy, number of foreign countries, foreign

sales ratio and the Herfindahl Index – in order to capture the different dimensions of

geographical diversification. We postulate that the different dimensions of geographical

diversification have a different effect on informational efficiency.

Table 1: FDI outflows, by region and economy

Region/economy USD‟ millions

2005 2006 2007 2008 2009

World 903,763 1,427,473 2,272,048 2,005,332 1,149,776

Developed economies 744,407 1 152196 1,890,419 1,600,707 828,005

Developing economies 139,934 244,703 330,033 344,033 273,401

Transition economies 19,422 30,573 51,596 60,591 48,368

Region/economy 2010 2011 2012 2013 2014

World 1,504,927 1 678 035 1 390 956 1,305,910 1,354,046

Developed economies 1,029,836 1 183088 909,383 833,630 822,826

Developing economies 413,219 422,066 426,081 380,784 468,184

Transition economies 61,871 72,879 55,491 91,496 63,072

Source: UNCTAD, World Investment Report 2015. (http://unctad.org/en/PublicationsLibrary/wir2015_en.pdf)

Foreign sales dummy is able to separate diversified firms and focused firms into two

groups and compare their influence on informational efficiency related to local news.

According to Chen (2005), individual investors and institutional investors prefer

information that is easy to understand and widely available. When a company undergoes

geographical diversification, its business coverage area becomes larger and the company is

exposed to other countries‟ risk where its business is involved. Its business structure

becomes more complex than that of a company that only focuses its sales locally (Morck

and Yeung 1991). As a result, they will not pay attention to a firm that has diversified in

many different foreign countries, which, consequently, will cause the potential investor base

of the diversified firm to become smaller. Eventually the informational efficiency will

become lower.

Hypothesis 1(a): Foreign sales dummy has a negative and significant effect on

informational efficiency related to local news.

Number of foreign countries captures the width of geographical diversification. In this

study, we suggest that the number of foreign countries will have the same result as the

foreign sales dummy, which has a negative effect on informational efficiency.

Hypothesis 1(b): Number of foreign countries has a negative and significant effect on

informational efficiency related to local news.

Foreign sales ratio shows the percentage of the total sales of a company that comes

from foreign countries. Foreign sales ratio measures the depth of geographical

diversification. We suggest that foreign sales ratio will have the same result as the foreign

sales dummy, which has a negative effect on informational efficiency.

Hypothesis 1(c): Foreign sales ratio has a negative and significant effect on informational

efficiency related to local news.

The Herfindahl Index is used to capture both the width and intensity of the geographical

diversification (Hitt et al. 1997; Denis et al. 2002). We find that geographical diversification

has two effects on the investor base of a firm, which will significantly affect its

informational efficiency. Firstly, the investor base of a company will increase by the

inclusion of foreign investors that recognize the company through its products or business in

a foreign country, which will subsequently increase its informational efficiency. Secondly,


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geographical diversification will cause a company‟s structure to become more complex.

This will cause fewer investors to pay attention to it since most of the investors prefer a

simple company that is easy to analyse. We suggest that the complexity effect will

outperform the foreign investors‟ recognition effect and Herfindahl Index will have a

negative effect on informational efficiency.

Hypothesis 1(d): Herfindahl Index has a negative and significant effect on informational

efficiency related to local news.

For Hypothesis 2, we investigate the effect of geographical diversification on the

informational efficiency related to global news instead of local news. First, we use the

foreign sales dummy as a proxy. Foreign sales dummy is able to separate diversified firms

and focused firms into two groups and compare their influence on informational efficiency

related to global news. Merton (1987), in his investor recognition theory, mentions that

investors only give consideration to a limited number of stocks and only exchange stocks

that they have information about. Consequently, stocks that are less known by investors

have a smaller potential speculator base. Because of the restricted consideration, speculators

can only consider a subset of all the accessible data. They generally do not consider or focus

on data from stocks that they do not take part in. As a result, if a company has sales in

foreign countries, it will be able to attract more attention from foreign investors and

eventually increase its informational efficiency. Lim and Hooy (2013) also used foreign

sales dummy as a moderator in the relationship between foreign shareholdings and

informational efficiency. They mentioned that diversified firms can increase their visibility

to foreign investors by diversifying their business to foreign countries. Their products will

become more recognizable to Foreign investors who will invest in their stocks. In our

research, we agree with this statement and suggest that the foreign sales dummy will

positively affect a firm‟s informational efficiency.

Hypothesis 2(a): Foreign sales dummy has a positive and significant effect on informational

efficiency related to foreign news.

Number of foreign countries captures the width of geographical diversification. We

postulate that the more countries that a firm diversifies in, the more foreign investors will

recognize the company. As a result, we suggest that the number of foreign countries will

have the same result as the foreign sales dummy, which has a positive effect on

informational efficiency.

Hypothesis 2(b): Number of foreign countries has a positive and significant effect on

informational efficiency related to foreign news.

Foreign sales ratio shows the percentage of the total sales of a company that comes

from foreign countries, and measures the depth of geographical diversification. We

postulate that the more foreign sales a company has in foreign countries, the larger the

potential foreign investor base it has. Therefore, we suggest that the foreign sales ratio will

have the same result as the foreign sales dummy, which has a positive effect on

informational efficiency.

Hypothesis 2(c): Foreign sales ratio has a positive and significant effect on informational


Herfindahl Index captures both the width and intensity of the geographical

diversification (Hitt et al. 1997; Denis et al. 2002). The investor base of a company that has

a higher value of Herfindahl Index will increase by the inclusion of foreign investors who

recognize the company through its products or business in a foreign country. This will

subsequently increase its informational efficiency. We suggest that the Herfindahl Index

will have the same result as the number of foreign countries and foreign sales ratio since it is

a combination of both and will have a positive effect on informational efficiency.


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Hypothesis 2(d): Herfindahl Index has a positive and significant effect on informational


The remainder of the paper is structured as follows. Section 2 discusses the variables

and model specification. Section 3 discusses the empirical results. Section 4 presents the

conclusion.

2. Methodology and Data

2.1 Measurement of Variables

In this subsection, the three main types of variable – dependent variable, independent

variable and control variable – are discussed and the methods used to compute each variable

are explained in detail.

2.1.1 Price Delay as Dependent Variable

Our construction of the local and global price delay measures follows the framework of Bae

et al. (2012), which involves the following unrestricted model:

(1)

where is the return on stock at week and denote the contemporaneous

and four weekly lagged returns on the local and world market indices, respectively. We

follow the convention in the price delay literature in utilizing weekly instead of monthly or

daily returns. As indicated by Hou and Moskowitz (2005), the dispersion for monthly data is

small because, generally, the information is incorporated into the stock price within one

month, whereas, for daily returns, there are numerous microstructure impacts that influence

the results, such as non-synchronous trading. The construction requires the following two

restricted models:

(2)

(3)

For each year, from 2005 through to 2014, we estimate equations (4) through (6) for every

firm in the sample. Their respective R-squares are used to calculate the scaled version of

stock price delay for firm i in year t:

(4)

(5)

( ) captures the variation in contemporaneous individual stock returns that

is explained by the lagged returns on the local (world) market index, where the latter is used

as a market-wide information signal. The greater the explanatory power of these lags, the

4 4

, , , , , ,

0 0

i t i i k m t k i k w t k i t

k k

r r r

,i tr i ,t ,m t kr ,w t kr

4

, 0 , , , ,

0

i t i i m t i k w t k i t

k

r r r

4

, , , 0 , ,

0

i t i i k m t k i w t i t

k

r r r


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longer the delay in responding to market-wide news that has common effects across firms.

The value of is limited between zero and one, with a value closer to zero (one)

showing the faster (slower) incorporation of information, and hence a higher (lower) degree

of stock price efficiency. The data required for the calculation of these stock price delay

measures are the weekly closing prices for individual stocks, the local market index and the

world market index. Following the common practice, weekly returns are calculated by

compounding daily returns between adjacent Wednesdays in order to avoid market

anomalies, such as the weekend and Monday effects (Bartholdy and Peare 2005). Hou and

Moskowitz (2005) contended that lower frequency data like monthly data will lead to

estimation error. For monthly data, there is little dispersion in the price delay measure since

the stock prices respond to information within a month. On the other hand, although high

level frequency data, such as daily data, provide more precision and greater dispersion in

price delay, the daily data are also influenced by confounding microstructure problems, such

as nonsynchronous trading and bid-ask bounce.

2.1.2 Geographical Diversification as Independent Variable

Geographical diversification has typically been measured in terms of the intensity of

international involvement and the geographic scope of international operations, as

highlighted by Lu and Beamish (2004). This study employs several types of geographical

diversification proxies in order to capture different aspects of geographical diversification.

Four different methods are used to measure geographical diversification in this study.

The first indicator used is the foreign sales dummy variable (DIVERSE). Firms with a

foreign sales to total sales ratio of more than 10% are classified as diversified. Firms that do

not fulfil the conditions are classified as focused (John and Ozgur 2006).

The second indicator that is used is the number of foreign countries (FCOUNTRY).

This indicator shows the total number of foreign countries in which a company diversifies

(Tallman and Li 1996).

The third indicator used is foreign sales ratio (FSALES). All the sales recorded outside

the country in which the company is registered are perceived as foreign sales (Tallman and

Li 1996).

FSALES = Foreign Sales/ Total Sales (6)

The fourth indicator used is the Herfindahl Index (HERFINDAHL), which is

constructed from foreign sales in each foreign country; this is a measure that has been

commonly used in many previous studies examining diversification issues (Hitt et al. 1997;

Denis et al. 2002). The Herfindahl index is calculated as follows for each firm i:

HERFINDAHL =1- Σ(Sales per country/Total sales) (7)

The Herfindahl Index ranges from 0 to 1. The closer the Herfindahl Index is to 1, the

more a firm‟s sales are diversified geographically, and the closer it is to 0, the more the

firm‟s sales are concentrated in a few countries.

2.1.3 Control Variables

The literature review of informational efficiency shows that numerous researchers have

concentrated on finding the determinants of price delay since Hou and Moskowitz (2005)

introduced the price delay model to quantify informational efficiency. Thus, there are a few

variables that are normally used as control variables in the price delay model. The four

DELAY


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control variables that are utilized in this study are firm size, trading volume,

liquidity/transaction costs and the number of security analyst's coverage.

First and foremost, firm size (lnMCAP) is measured using the natural logarithm of the

annual market capitalization of a company at the end of the calendar year. Previous

researchers, such as Lim and Hooy (2010), Phillips (2010), Saffi and Sigurdson (2011), and

Hou and Moskowitz (2005), incorporated firm size as a control variable in their study.

Secondly, since Chordia and Swaminathan (2000) demonstrated that high trading

volume stocks tend to be promptly changed in accordance with new market information

compared to low trading volume stocks, trading volume has turned into an imperative

determinant for price delay. For example, Bae et al. (2012), Callen et al. (2013), and Lim

and Hooy (2010), included this control variable in their study of informational efficiency. In

this study, trading volume (VOLUME) is proxied by the average monthly share trading

volume scaled by total shares outstanding for each firm and year.

Thirdly, the proxy for volatility is the standard deviation of weekly returns for a year

(VOLATILITY) (Bae et al. 2012). Thomson Reuters DataStream gives the firm-level panel

data of annual market capitalization, monthly share volume, and weekly closing stock

prices.

Fourthly, analyst coverage is one of the most vital control variables, and has been

incorporated by researchers such as Hou and Moskowitz (2005), and Bae et al. (2012) in

their price delay model. From the perspective of the data given by the Institutional Brokers

Estimate System (I/B/E/S), the number of analysts issuing earnings forecasts (ANALYST)

for a firm each year is collected. Analyst coverage is written as being equivalent to zero for

a firm-year observation if a firm is not listed on the I/B/E/S database or does not have

earnings forecasts for any given year.

2.2 Model Specification

In this study, we use multiple regression to test the hypotheses in this study. Multiple

regression is used when there are many independent variables but only one dependent

variable. The independent variables in this study include a geographical diversification

proxy, which acts as the subject variable and other prominent control variables from

previous literature. Figure 1 shows the empirical framework.

For the control variables, we include several important variables – firm size, trading

volume, liquidity and analyst forecasts. These variables have been proven to have a

significant effect on price delay in the previous literature pertaining to informational

efficiency. The OLS estimator is a method for estimating a well fitted regression line by

minimizing the residual sum of squares. OLS is appropriate in this study as it is the most

straightforward regression technique, and the estimation is reliable as long as common

regression problems are accounted for. The pooled ordinary least squares (OLS) regression

model is specified as follows:

( )

(8)

2.3 Data Analysis

The data are considered as panel data as they involve public listed firms in emerging

countries that span across 10 years. We choose to collect the data as unbalanced data as each

firm may have a varying number of observations. The reason for this is that in many

countries, geographical diversification data do not have to be disclosed in the annual report

but are left to the free will of each respective firm. Therefore, we predict that there will be

incomplete data across the period that we study. Our panel data represent a short panel as


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Figure 1: Empirical framework

there are large number of firms with a short time period. The number of firm observations is

larger than the number of year observations.

2.3.1 Sample Data

Our sample includes public listed companies in emerging countries following the IMF list.

However, due to the unavailability of data for the sales data and analysts‟ forecasts

according to the geographic segment, some countries have been excluded from our sample

study. As a result, there are only 12 countries remaining, which are Argentina, Brazil, Chile,

China, India, Indonesia, Malaysia, Mexico, Philippines, South Africa, Thailand and Turkey.

To construct the price delay variables, which are the dependent variables in the study,

we require stock prices for each public listed company, as well as the local stock index for

each emerging country and the MSCI stock index. To construct geographical diversification

proxies, the foreign sales of each public listed company in the emerging countries and the

foreign countries that the company has diversified in are needed. To construct the control

variables, the market capital, stock volumes, stock daily returns and number of analysts that

study the company are required. Finally, for further study, the company components of each

local index and the number of industries in which the company is involved are needed.

2.3.2 Time Span

The time span of this sample study ranges from year 2005 to year 2014. Since our sample

data concentrate on public listed companies in emerging countries, during this time span, the

OFDI of emerging countries has increased by a considerable amount (World Investment

Report). The increase in OFDI shows that many companies in emerging countries are

actively involved in geographical diversification to foreign countries. The objective of this

study is to analyse the effect of geographical diversification on informational efficiency.

The considerable increase in OFDI will improve the significance of the results.

3. Results and Discussions

3.1 Descriptive Statistics

There are three tables for the overall descriptive statistics. Table 2 describes all the

important variables used in this study. Table 3 shows the descriptive statistics of all the

variables and compares the mean and t-test of each variable between the diversified and the

non-diversified firms. Table 4 shows the correlation matrix for the important variables.

Table 3 shows the descriptive statistics of all the important variables used in this study.

In section (1), important descriptive statistics like mean, median, standard deviation,

minimum value, maximum value and number of observations of the whole sample study are


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Table 2: Description of variables

Variable Name Variable Description

Dependent Variables

Local Delay

(DELAYL)

Stock price delay relative to local market information, which is constructed

using equation (7)

Global Delay

(DELAYG)

Stock price delay relative to global market information, which is constructed

using equation (8)

Independent Variables

Foreign Sales

Dummy

(DIVERSE)

Dummy variable, which is equal to 1 if the company has more than 10% sales

in foreign countries

Number Foreign

Countries

(FCOUNTRY)

Number of foreign countries that a firm diversifies in

Foreign Sales Ratio

(FSALES)

Ratio of foreign sales to total sales

Herfindahl Index

(HERFINDAHL)

Herfindahl Index is constructed using equation (10)

Control Variables

Firm Size

(lnMCAP)

Natural logarithm of market capital of equity in millions of US dollars

Trading Volume

(VOLUME)

Average number of shares traded monthly scaled by total shares outstanding

Volatility

(VOLATILITY)

Standard deviation of weekly returns for a year

Number of

Analysts

(ANALYST)

Number of analyst forecasts that is reported by the Institutional Brokers‟

Estimate System

displayed. In sections (2) and (3), the descriptive statistics of the variables are further

divided according to whether the firm is diversified or non-diversified. The final section

shows the t-test results regarding the difference in the mean between the diversified and

non-diversified firms for all the variables.

From section (1), the delay in the stock price relative to the local index (DELAYL) has

a mean value of 0.413, which is higher than 0.218, the delay in the stock price relative to

global index (DELAYG). This shows that the stock price generally reacts slower to the local

index compared to the global index. For the independent variables, all four geographical

diversification proxies show a median of zero, which indicates that more than half of the

companies in our sample study are non-diversified. Firm size (MCAP) has a large different

value between the mean (US$1238m) and median (US$111m), which indicates that the

tabulation of firm size is skewed to the right with the majority of firms having a small firm

size and a small group of firms having a very large firm size. The monthly average trading

volume (VOLUME) also shows a large deviation between the mean (0.133) and the median

(0.038), which indicates that although most of the stocks are traded normally there is a small

group of stocks that are actively traded. The number of analysts (ANALYST) has a median

of zero, which shows that more than half of the stocks are not covered by analysts reports by

the Institutional Brokers‟ Estimate System. In sections (2) and (3), diversified firms

generally have a smaller delay than non-diversified firms irrespective of whether relative to

local or global information at the 1% significance level from the t-test. The relationship

between diversification and price delay is further tested using the regression model. All four


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geographical diversification proxies show consistent descriptive statistics since the number

of foreign countries, foreign sales ratio and Herfindahl index show a higher mean value for

diversified firms compared to non-diversified firms with a t-test significance level of 1%.

Diversified firms record a firm size mean value of US$2066m, which is higher than the

US$1132m for non-diversified firms. This is reasonable because multinational companies

usually have a larger firm size as their business segments have expanded to other foreign

countries all over the world. The difference between the monthly average trading volume

between diversified and non-diversified firms is negligible and does not pose a significant t-

test result. The volatility of the weekly stock return for diversified firms is lower than that

for non-diversified firms because diversified firms can reduce their unsystematic risks

through diversification to other foreign countries. Lastly, there is more analysts‟ coverage

on diversified firms compared to non-diversified firms, which shows the preference of

analysts towards diversified firms.

3.2.1 OLS, Fixed Effects and Random Effects

Three different regression models – ordinary least squares model, fixed effects model and

random effects model – are run for all regression models, which include the regression

model with control variables only and the regression model with different types of

geographical diversification proxies including foreign sales dummy, number of foreign

countries, foreign sales ratio and Herfindahl Index. Most of the results show an inconsistent

sign and a significant level among the coefficient of variables. For example, DIVERSE. The

FCOUNTRY and HERFINDAHL are significant at 1% in all three models but have

different signs. As a result, it is appropriate to determine which model is the most suitable.

The results of all the regression models are not shown in this table due to space constraints.

3.2.2 Breusch and Pagan Lagrangian Multiplier Test

The Breusch and Pagan Lagrangian multiplier tests are used to identify whether normal

OLS or the random effects model is a better choice for the regression. If the significance

level is below 10%, it suggests that the random effects model should be chosen over normal

OLS. All regression models attain a significance level below 1%, which means that the

random effects model is preferable to the normal OLS for all models.

Table 5: Summary results of Breusch and Pagan Lagrangian Multiplier tests

Chi2 Control

model

Foreign

Sales

Dummy

Model

Number of

Foreign Countries

Model

Foreign

Sales Ratio

Model

Herfindahl

Index Model

Local Delay 114.79*** 120.35*** 114.72*** 121.02*** 115.42***

Global Delay 8663.76*** 4593.97*** 4595.50*** 4591.25*** 4593.37***

. ***, ** and * denote statistical significance at the 1%, 5% and 10% levels, respectively.

3.2.3 Hausman Test

Hausman test is used to identify whether the random effects model or the fixed effects

model is a better choice for the regression. If the significance level is below 10%, it

suggests that the fixed effects model should be chosen over the random effects model. All

the regression models have a significance level below 1%, which means that the fixed

effects model is preferable to the random effects model for all models.


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Table 6: Summary results of Hausman test

Chi2 Control

model

Foreign Sales

Dummy

Model

Number of

Foreign

Countries Model

Foreign Sales

Ratio Model

Herfindahl

Index Model

Local Delay 363.90*** 328.77*** 341.39*** 313.45*** 337.16***

Global

Delay

148.62*** 114.64*** 115.84*** 116.12*** 115.74***

. ***, ** and * denote statistical significance at the 1%, 5% and 10% levels, respectively.

3.2.4 Fixed Effects Model

After running the Breusch and Pagan Lagrangian multiplier tests and the Hausman test, the

results suggest that the fixed effects model is the most suitable for our study for both local

delay and global delay. Table 7 and Table 8 show the fixed effects model for four different

geographical diversification proxies for both local and global delay.

Table 7 shows the fixed effects models for local delay with different geographical

diversification proxies. All the geographical diversification proxies show a significant and

positive effect on local delay, where DIVERSE, FCOUNTRY and HERFINDAHL show

significant at 1% and FSALES show significant at 5%. The results shown are consistent

with our hypothesis, which postulates that geographical diversification will increase the

local delay since investors do not favour companies that have a complex business structure.

When a company undergoes geographical diversification, its business coverage area

becomes larger and the company is exposed to other countries‟ risk in which its business is

involved. Its business structure becomes more complex than that of a company that only

focuses its sales locally (Morck and Yeung 1991). Since individual investors and

institutional investors prefer information that is easy to understand and widely available

(Chen 2005), many investors will abandon their research on diversified companies, which

will cause their potential investor base to become small and eventually decrease their

informational efficiency to local news. Due to the cost of information (Shapiro 2002), when

the information on a company or stock is hard to acquire or analyse, its informational

efficiency related to local news will decrease since most of the institutional investors are

more concerned about local news.

For the control variables, firm size (lnMCAP) shows a negative and significant effect

on local delay, which is consistent with previous literature (Bae et al. 2012). Firm size is an

important and prevalent determinant of delay, which is often used in previous literature,

such as Lim and Hooy (2010), Phillips (2010), Saffi and Sigurdson (2011) and Hou and

Moskowitz (2005). Larger firms usually have higher visibility and therefore less delay. The

monthly average trading volume (VOLUME) shows a positive and significant effect on

local delay. Stock return volatility shows a negative and significant result on local delay.

This result is consistent with previous literature since stocks with higher volatility will

attract more investors and therefore have less delay (Bae et al. 2012). It is surprising that the

number of analyst forecasts (ANALYST) shows a positive and significant result, since more

analyst forecasts should mean higher visibility of the firms.

We postulate that the positive result may be due to investors being strongly affected by

the forecast made by analysts until it causes a stock price delay to the local index. For

example, when the stock market crashes, many investors still believe in the analysts‟

forecast of a particular share and hold on to it; such a situation will cause the stock price to

experience a price delay relative to the local index.

Table 8 shows the fixed effects models for global delay with different geographical

diversification proxies. All the geographical diversification proxies – DIVERSE,

FCOUNTRY, FSALES and HERFINDAHL – show an insignificant effect on global delay.


28

Table 7: Fixed effects model for local delay This table shows the fixed effects model for local delay with the control variables and different geographical

diversification proxies. Column (1) only includes the control variables, while column (2), (3), (4), (5)

includes the control variables and different geographical diversification proxies, which are foreign sales dummy, number of foreign countries, foreign sales ratio and Herfindahl Index. The descriptions of all the

variables in this table are provided in Table 2. The regressions in this table are based on the following

equation:

(1) (2) (3) (4) (5)

CONSTANT 0.4301*** 0.4915*** 0.4918*** 0.4926*** 0.4904***

(0.0076) (0.0119) (0.0119) (0.0119) (0.0119)

lnMCAP -0.0002 -0.0074*** -0.0077*** -0.0074*** -0.0076***

(0.0014) (0.0021) (0.0021) (0.0021) (0.0021)

VOLUME 0.0088*** 0.0123*** 0.0123*** 0.0123*** 0.0123***

(0.0022) (0.0037) (0.0037) (0.0037) (0.0037)

VOLATILITY -0.4138*** -0.7170*** -0.7160*** -0.7188*** -0.7160***

(0.0288) (0.0408) (0.0408) (0.0408) (0.0408)

ANALYST 0.0045*** 0.0046*** 0.0046*** 0.0046*** 0.0045***

(0.0004) (0.0005) (0.0005) (0.0005) (0.0005)

DIVERSE 0.0160***

(0.0051)

FCOUNTRY 0.0073***

(0.0018)

FSALES 0.0281**

(0.0117)

HERFINDAHL 0.0524***

(0.0127)

N 49626 29339 29339 29339 29339

Adjusted R2 -0.123 -0.188 -0.1877 -0.1882 -0.1877

R2 0.0082 0.0173 0.0175 0.0171 0.0175

Notes: The standard error of each coefficient is reported in parentheses. N denotes the number of observations

Adjusted R2 represents the adjusted R-squared, while R2 represents R-squared. ***, ** and * denote the statistical significance at the 1%, 5% and 10% levels, respectively.

The results show that whether a firm is geographical diversified or non-diversified does

not have any important effect on global delay. The different effects of the geographical

diversification proxies towards local delay and global delay suggest that there are two

different groups of investors who are monitoring the stock prices. The first group of

investors who take the local index into consideration when choosing stocks are strongly

affected by the geographical diversification decision of a firm. When a firm undergoes

geographical diversification, it will abandon the stock and will not make buy or sell

decisions on that stock, which causes the stock to become informationally inefficient. The

second group of investors who take global index into consideration when choosing stocks is

indifferent to whether or not a firm is geographically diversified.

For the control variables, firm size (lnMCAP) shows a negative and significant effect

on global delay, which is consistent with the result of the local delay model. The monthly

average trading volume (VOLUME) shows an insignificant effect on global delay. Stock

return volatility shows a positive and significant result on global delay. This result shows

that investors who emphasize global news do not favour stock with high volatility because

high volatility means high risk. The number of analyst forecasts (ANALYST) shows a


29

Table 8: Fixed effects model for global delay This table shows the fixed effects model for global delay with the control variables and different geographical

diversification proxies. Column (1) only includes the control variables, while columns (2), (3), (4), (5) include the

control variables and different geographical diversification proxies, which are foreign sales dummy, number of foreign countries, foreign sales ratio and Herfindahl Index. The descriptions of all the variables in this table are

provided in Table 2. The regressions in this table are based on the following equation:

(1) (2) (3) (4) (5)

CONSTANT 0.2517*** 0.2246*** 0.2240*** 0.2238*** 0.2240***

(0.0063) (0.0094) (0.0094) (0.0094) (0.0094)

lnMCAP -0.0064*** -0.0056*** -0.0057*** -0.0057*** -0.0057***

(0.0012) (0.0016) (0.0016) (0.0016) (0.0016)

VOLUME -0.0022 -0.0009 -0.0009 -0.0009 -0.0009

(0.0018) (0.0029) (0.0029) (0.0029) (0.0029)

VOLATILITY 0.2710*** 0.3128*** 0.3133*** 0.3132*** 0.3133***

(0.0241) (0.0322) (0.0322) (0.0322) (0.0322)

ANALYST -0.0015*** -0.0008** -0.0008** -0.0008** -0.0008**

(0.0004) (0.0004) (0.0004) (0.0004) (0.0004)

DIVERSE -0.0028

(0.0040)

FCOUNTRY 0.0004

(0.0014)

FSALES 0.0061

(0.0092)

HERFINDAHL 0.0018

(0.0101)

N 49626 29339 29339 29339 29339

Adjusted R2 -0.1263 -0.202 -0.2021 -0.202 -0.2021

R2 0.0053 0.0057 0.0056 0.0056 0.0056


Adjusted R2 represents the adjusted R-squared, while R2 represents R-squared. ***, ** and * denote the

statistical significance at the 1%, 5% and 10% levels, respectively.

negative and significant result, which is consistent with previous literature since more

analyst forecasts means higher visibility of the firms (Bae et al. 2012).

3.3 Robustness Test

The robustness test of this study can be divided into four sections. In the first section, the

white standard error is employed to solve the heteroscedasticity problems. VIF proxies are

used in the second section to show that our regression results do not face serious

multicollinearity problems. In the third section, we employ an alternative measure for both

local and global delay to show that our dependent variables are robust to other

measurements of delay. In the last section, we employ a crisis dummy to control for the

financial crisis effect.

3.3.1 White Standard Error

We solve the heteroscedasticity problem by using the White standard error, as suggested by

White (1980). The White standard error is used to solve the within cross-sectional

correlation and arbitrary heteroscedasticity issues. Table 9 shows the results of the local

delay for the fixed effects model using the White standard error. The results show that

although the standard error of our subject variables do increase (standard error of DIVERSE


30

Table 9: White standard error for local delay This table employs the White standard error on the fixed effects model of local delay, as shown in Table 7. Column

(1) only includes the control variables, while columns (2), (3), (4), (5) include the control variables and different

geographical diversification proxies, which are foreign sales dummy, number of foreign countries, foreign sales ratio and the Herfindahl Index. The descriptions of all the variables in this table are provided in Table 2. The

regressions in this table are based on the following equation:

(1) (2) (3) (4) (5)

CONSTANT 0.4301*** 0.4915*** 0.4918*** 0.4926*** 0.4904***

(0.0084) (0.0136) (0.0136) (0.0136) (0.0136)

lnMCAP -0.0002 -0.0074*** -0.0077*** -0.0074*** -0.0076***

(0.0015) (0.0024) (0.0024) (0.0024) (0.0024)

VOLUME 0.0088 0.0123** 0.0123** 0.0123** 0.0123**

(0.0061) (0.0058) (0.0058) (0.0059) (0.0058)

VOLATILITY -0.4138*** -0.7170*** -0.7160*** -0.7188*** -0.7160***

(0.0361) (0.0518) (0.0515) (0.0517) (0.0517)

ANALYST 0.0045*** 0.0046*** 0.0046*** 0.0046*** 0.0045***

(0.0004) (0.0005) (0.0005) (0.0005) (0.0005)

DIVERSE 0.0160***

(0.0054)

FCOUNTRY 0.0073***

(0.0019)

FSALES 0.0281**

(0.0128)


(0.0135)

N 49626 29339 29339 29339 29339

Adjusted R2 0.0081 0.0171 0.0173 0.0169 0.0174

R2 0.0082 0.0173 0.0175 0.0171 0.0175




increases from 0.0051 to 0.0054, standard error of FCOUNTRY increases from 0.0018 to

0.0019, standard error of FSALES increases from 0.0117 to 0.0128, standard error of

HERFINDAHL increases from 0.0127 to 0.0135), the significant level remains unchanged.

As a result, we can conclude that the result remains significant after accounting for the

heteroscedasticity problem.

Table 10 shows the results of the global delay for the fixed effects model using the

White standard error to solve the heteroscedasticity problem. The results show that all the

geographical diversification proxies remain insignificant.

3.3.2 Multicollinearity Problem

We use the VIF indicator to show whether our regression models have multicollinearity

problems. If the VIF indicator shows a value of more than 5 or 10, it shows that there is a

serious multicollinearity problem in the regression model (O‟Brien 2007). The VIF proxies

for all the variables used in all the models have a value of less than 5, with the highest being

1.45, which is lnMCAP. The results show that there are no serious multicollinearity

problems present in our regression models.


31

Table 10: White standard error for global delay This table employs the White standard error on the fixed effects model of global delay, as shown in Table 8.

Column (1) only includes the control variables, while columns (2), (3), (4), (5) include the control variables and the

different geographical diversification proxies, which are foreign sales dummy, number of foreign countries, foreign sales ratio and the Herfindahl Index. The descriptions of all the variables in this table are provided in Table 2. The

regressions in this table are based on the following equation:

(1) (2) (3) (4) (5)

CONSTANT 0.2517*** 0.2246*** 0.2240*** 0.2238*** 0.2240***

(0.0070) (0.0104) (0.0103) (0.0104) (0.0104)

lnMCAP -0.0064*** -0.0056*** -0.0057*** -0.0057*** -0.0057***

(0.0012) (0.0018) (0.0018) (0.0018) (0.0018)

VOLUME -0.0022* -0.0009 -0.0009 -0.0009 -0.0009

(0.0012) (0.0021) (0.0021) (0.0021) (0.0021)

VOLATILITY 0.2710*** 0.3128*** 0.3133*** 0.3132*** 0.3133***

(0.0289) (0.0415) (0.0416) (0.0416) (0.0415)

ANALYST -0.0015*** -0.0008** -0.0008** -0.0008** -0.0008**

(0.0003) (0.0004) (0.0004) (0.0004) (0.0004)

DIVERSE -0.0028

(0.0042)

FCOUNTRY 0.0004

(0.0016)

FSALES 0.0061

(0.0106)

HERFINDAHL 0.0018

(0.0109)

N 49626 29339 29339 29339 29339

Adjusted R2 0.0052 0.0055 0.0055 0.0055 0.0055

R2 0.0053 0.0057 0.0056 0.0056 0.0056


Adjusted R2 represents the adjusted R-squared, while R2 represents R-squared. ***, ** and * denote the statistical significance at the 1%, 5% and 10% levels, respectively.

Table 11: Summary results of VIF proxies VIF Control Model Foreign Sales

Dummy Model

Number of Foreign

Countries Model

Foreign Sales

Ratio Model

Herfindahl

Index Model

lnMCAP 1.42 1.44 1.45 1.44 1.44

VOLUME 1.01 1.01 1.01 1.01 1.01

VOLATILITY 1.1 1.09 1.09 1.09 1.09 ANALYST 1.32 1.34 1.34 1.34 1.34

DIVERSE 1.02

FCOUNTRY 1.02 FSALES 1.01

HERFINDAHL 1.02

Mean 1.21 1.18 1.18 1.18 1.18

3.3.3 Alternative Measure for Delay

Hou (2007) suggested that an alternative measure of delay can be used to replace the

original delay measure, which is calculated using equations (4) and (5). The alternative price

delay measure is indicated in the following equation:

( ) (9)


32

According to Hou (2007), there are a few engaging properties on this transformed version of

price delay measure. First, it is monotonic in x. Second, the logistics transformation of price

delay helps to remove the excess skewness and kurtosis of the original price delay measure.

Finally, the values of this transformed delay measure are not being restricted within the

intervals [0.1].

Table 12 shows the local delay fixed effects regression model using the alternative

delay measure constructed using equation (9). The results show that all the geographical

diversification proxies remain significant and that the coefficients of all the variables remain

unchanged.

Table 13 shows the global delay fixed effects regression model by using the alternative

delay measure constructed using equation (9). The results show that all the geographical

diversification proxies remain insignificant and the coefficients of the all the variables

remain unchanged unless VOLUME becomes significant at 5%.

In conclusion, we suggest that by using the alternative delay measure, most of the

coefficient signs and significant levels of control variables do not change. The significant

level and coefficient signs of all the geographical diversification proxies remain the same.

3.3.4 Controlling for Crisis

From the descriptive statistics across years for the control variables (Figure 8, 10, 12), the

results show that there is a huge fluctuation during Year 2008 and Year 2009. We postulate

that the fluctuation is due to the sub-prime financial crisis, which occurred during Year 2008

and Year 2009. To control for the financial crisis, we add a crisis dummy variable for all the

observations in Year 2008 and Year 2009.

Table 14 shows the local delay fixed effects regression model after including the crisis

dummy for Year 2008 and Year 2009. The results of all the geographical diversification

proxies remain unchanged and significant. CRISIS has a negative effect on DELAYL

because during a crisis, investors are more alert to the local news and local index. Their

decision to buy and sell shares will highly depend on the local index.

Table 15 shows the global delay fixed effects regression model after including the

crisis dummy for Year 2008 and Year 2009. The results of all the geographical

diversification proxies. remain insignificant. CRISIS has a negative effect on DELAYG

because during a crisis, investors are more alert to the global news and global index. Their

decision to buy and sell shares will highly depend on the global index.

4. Conclusion

All four geographical diversification proxies – foreign sales dummy, number of foreign

countries, foreign sales ratio and the Herfindahl Index – show a positive and significant

effect on local delay but an insignificant effect on global delay. Firm size, volatility and

number of analyst forecasts show significant effects on both local and global delay whereas

trading volume only shows a significant effect on local delay. After running the Breusch and

Pagan Lagrangian multiplier tests and the Hausman test, the results suggest that the fixed

effects model is most suitable compared to the OLS model and random effects model. All

the regression models in this study are robust to heteroscedasticity and multicollinearity

problems. All geographical diversification proxies remain significant towards local delay

when we use alternative delay measures and control for financial crisis. The t-tests

regarding the difference in the mean between diversified and non-diversified firms for all

the variables show significant results except for trading volume.


33

Table 12: Alternative measure for local delay This table employs the alternative measure of local delay generated using equation (9) for the fixed effects model

shown in Table 7. Column (1) only includes the control variables, while columns (2), (3), (4), (5) include the

control variables and different geographical diversification proxies, which are foreign sales dummy, number of foreign countries, foreign sales ratio and Herfindahl Index. The descriptions of all the variables in this table are


(1) (2) (3) (4) (5)

CONSTANT -0.3710*** -0.0642 -0.0626 -0.0582 -0.0686

(0.0377) (0.0585) (0.0584) (0.0584) (0.0585)

lnMCAP 0.0033 -0.0320*** -0.0334*** -0.0319*** -0.0329***

(0.0069) (0.0103) (0.0103) (0.0103) (0.0103)

VOLUME 0.0378*** 0.0612*** 0.0612*** 0.0613*** 0.0612***

(0.0108) (0.0181) (0.0181) (0.0181) (0.0181)

VOLATILITY -1.8852*** -3.4399*** -3.4349*** -3.4487*** -3.4357***

(0.1431) (0.2012) (0.2012) (0.2012) (0.2012)

ANALYST 0.0212*** 0.0215*** 0.0214*** 0.0215*** 0.0214***

(0.0022) (0.0025) (0.0025) (0.0025) (0.0025)

DIVERSE 0.0769***

(0.0250)

FCOUNTRY 0.0354***

(0.0090)

FSALES 0.1278**

(0.0576)


(0.0628)

N 49626 29339 29339 29339 29339

Adjusted R2 -0.1242 -0.1891 -0.1888 -0.1893 -0.1889

R2 0.0072 0.0163 0.0166 0.0162 0.0165

Notes: The standard error of each coefficient is reported in parentheses. N denotes the number of observations Adjusted R2 represents the adjusted R-squared, while R2 represents R-squared. ***, ** and * denote the



34

Table 13: Alternative measure for global delay This table employs the alternative measure for global delay generated using equation (9) for the fixed effects

model, as shown in Table 8. Column (1) only includes the control variables, while columns (2), (3), (4), (5) include

the control variables and different geographical diversification proxies, which are foreign sales dummy, number of foreign countries, foreign sales ratio and the Herfindahl Index. The descriptions of all the variables in this table are


(1) (2) (3) (4) (5)

CONSTANT -1.3515*** -1.5477*** -1.5529*** -1.5547*** -1.5527***

(0.0411) (0.0650) (0.0650) (0.0650) (0.0651)

lnMCAP -0.0419*** -0.0311*** -0.0317*** -0.0321*** -0.0317***

(0.0075) (0.0114) (0.0114) (0.0114) (0.0114)

VOLUME -0.0245** -0.0469** -0.0470** -0.0470** -0.0470**

(0.0118) (0.0201) (0.0201) (0.0201) (0.0201)

VOLATILITY 1.2593*** 1.4748*** 1.4786*** 1.4788*** 1.4785***

(0.1563) (0.2238) (0.2238) (0.2238) (0.2238)

ANALYST -0.0120*** -0.0089*** -0.0090*** -0.0091*** -0.0090***

(0.0024) (0.0028) (0.0028) (0.0028) (0.0028)

DIVERSE -0.0332

(0.0278)

FCOUNTRY -0.0004

(0.0100)

FSALES 0.0345

(0.0641)

HERFINDAHL -0.0039

(0.0699)

N 49626 29339 29339 29339 29339

Adjusted R2 -0.1277 -0.2045 -0.2046 -0.2046 -0.2046

R2 0.004 0.0036 0.0035 0.0036 0.0035


Adjusted R2 represents the adjusted R-squared, while R2 represents R-squared. ***, ** and * denote the statistical significance at the 1%, 5% and 10% levels, respectively


35

Table 14: Crisis controlling dummy for local delay This table adds in a crisis dummy for all the observations in Year 2008 and Year 2009 for the fixed effects model


control variables and different geographical diversification proxies, which are foreign sales dummy, number of foreign countries, foreign sales ratio and the Herfindahl Index. The descriptions of all the variables in this table are


(1) (2) (3) (4) (5)

CONSTANT 0.4363*** 0.5098*** 0.5101*** 0.5109*** 0.5087***

(0.0076) (0.0119) (0.0119) (0.0119) (0.0119)

lnMCAP -0.0016 -0.0115*** -0.0117*** -0.0115*** -0.0117***

(0.0014) (0.0021) (0.0021) (0.0021) (0.0021)

VOLUME 0.0091*** 0.0119*** 0.0119*** 0.0119*** 0.0118***

(0.0022) (0.0037) (0.0037) (0.0037) (0.0037)

VOLATILITY -0.3455*** -0.5501*** -0.5496*** -0.5515*** -0.5491***

(0.0300) (0.0429) (0.0429) (0.0429) (0.0429)

ANALYST 0.0045*** 0.0046*** 0.0046*** 0.0046*** 0.0046***

(0.0004) (0.0005) (0.0005) (0.0005) (0.0005)

CRISIS -0.0181*** -0.0379*** -0.0378*** -0.0380*** -0.0379***

(0.0022) (0.0030) (0.0030) (0.0030) (0.0030)

DIVERSE 0.0159***

(0.0051)

FCOUNTRY 0.0070***

(0.0018)

FSALES 0.0293**

(0.0117)


(0.0127)

N 49626 29339 29339 29339 29339

Adjusted R2 -0.1214 -0.1805 -0.1803 -0.1807 -0.1802

R2 0.0096 0.0235 0.0237 0.0233 0.0237


Adjusted R2 represents the adjusted R-squared, while R2 represents R-squared. ***, ** and * denote the statistical

significance at the 1%, 5% and 10% levels, respectively


36

Table 15: Crisis controlling dummy for global delay This table adds in a crisis dummy for all the observations in Year 2008 and Year 2009 for the fixed effects model


control variables and different geographical diversification proxies, which are foreign sales dummy, number of foreign countries, foreign sales ratio and the Herfindahl Index. The descriptions of all the variables in this table are


(1) (2) (3) (4) (5)

CONSTANT 0.2710*** 0.2429*** 0.2424*** 0.2421*** 0.2424***

(0.0063) (0.0094) (0.0094) (0.0094) (0.0094)

lnMCAP -0.0108*** -0.0097*** -0.0097*** -0.0098*** -0.0097***

(0.0012) (0.0017) (0.0017) (0.0017) (0.0017)

VOLUME -0.0013 -0.0013 -0.0013 -0.0013 -0.0013

(0.0018) (0.0029) (0.0029) (0.0029) (0.0029)

VOLATILITY 0.4862*** 0.4803*** 0.4807*** 0.4807*** 0.4807***

(0.0248) (0.0338) (0.0338) (0.0338) (0.0338)

ANALYST -0.0015*** -0.0008** -0.0008** -0.0008** -0.0008**

(0.0004) (0.0004) (0.0004) (0.0004) (0.0004)

CRISIS -0.0570*** -0.0380*** -0.0380*** -0.0380*** -0.0380***

(0.0019) (0.0024) (0.0024) (0.0024) (0.0024)

DIVERSE -0.0029

(0.0040)

FCOUNTRY 0.0002

(0.0014)

FSALES 0.0073

(0.0092)

HERFINDAHL 0.0015

(0.0100)

N 49626 29339 29339 29339 29339

Adjusted R2 -0.1025 -0.1898 -0.1898 -0.1898 -0.1898

R2 0.0263 0.0158 0.0158 0.0158 0.0158




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Effect of Geographical Diversification on Informational Efficiency … · geographical diversification with similar business operations in different countries as the main corporate